cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Alfian Ma'arif
Contact Email
alfian.maarif@te.uad.ac.id
Phone
-
Journal Mail Official
ijrcs@ascee.org
Editorial Address
Jalan Janti, Karangjambe 130B, Banguntapan, Bantul, Daerah Istimewa Yogyakarta, Indonesia
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
International Journal of Robotics and Control Systems
Published by Association for Scientific Computing Electronics and Engineering
ISSN : -     EISSN : 27752658     DOI : https://doi.org/10.31763/ijrcs
Core Subject : Engineering,
Control & Systems Engineering Electrical & Electronics Engineering
International Journal of Robotics and Control Systems is open access and peer-reviewed international journal that invited academicians (students and lecturers), researchers, scientists, and engineers to exchange and disseminate their work, development, and contribution in the area of robotics and control technology systems experts. Its scope includes Industrial Robots, Humanoid Robot, Flying Robot, Mobile Robot, Proportional-Integral-Derivative (PID) Controller, Feedback Control, Linear Control (Compensator, State Feedback, Servo State Feedback, Observer, etc.), Nonlinear Control (Feedback Linearization, Sliding Mode Controller, Backstepping, etc.), Robust Control, Adaptive Control (Model Reference Adaptive Control, etc.), Geometry Control, Intelligent Control (Fuzzy Logic Controller (FLC), Neural Network Control), Power Electronic Control, Artificial Intelligence, Embedded Systems, Internet of Things (IoT) in Control and Robot, Network Control System, Controller Optimization (Linear Quadratic Regulator (LQR), Coefficient Diagram Method, Metaheuristic Algorithm, etc.), Modelling and Identification System.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 25 Documents
 1   2   3 
Search results for , issue "Vol 5, No 3 (2025)" : 25 Documents clear
Recent Advances in Artificial Intelligence for Dyslexia Detection: A Systematic Review Pamungkas, Yuri; Rangkuti, Rahmah Yasinta; Karim, Abdul; Sangsawang, Thosporn
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.2057

Abstract

The prevalence of dyslexia, a common neurodevelopmental learning disorder, poses ongoing challenges for early detection and intervention. With the advancement of artificial intelligence (AI) technologies in the fields of healthcare and education, AI has emerged as a promising tool for supporting dyslexia screening and diagnosis. This systematic review aimed to identify recent developments in AI applications for dyslexia detection, focusing on the methods used, types of algorithms, datasets, and their performance outcomes. A comprehensive literature search was conducted in 2025 across databases including ScienceDirect, IEEE Xplore, and PubMed using a combination of relevant MeSH terms. The article selection process followed the PRISMA guidelines, resulting in the inclusion of 31 eligible studies. Data were extracted on AI approaches, algorithm types, dataset characteristics, and key performance metrics. The results revealed that machine learning (ML) was the most widely applied method (58.06%), followed by multi-method (22.58%), deep learning (16.13%), and large language models (3.23%). Among the ML algorithms, Random Forest and Decision Tree were the most commonly used due to their robustness and performance on structured datasets. In the deep learning category, CNN were the most frequently used models, especially for image-based and sequential input data. The datasets varied widely, including digital cognitive tasks, EEG, MRI, handwriting, and eye-tracking data, with several studies employing multimodal combinations. Ensemble and hybrid models demonstrated superior performance, with some achieving accuracy rates exceeding 98%. This review highlights that AI, particularly ML and multimodal ensemble methods, holds strong potential for improving the accuracy, scalability, and accessibility of dyslexia detection. Future research should prioritize large-scale, multimodal datasets, interpretable models, and adaptive learning systems to enhance real-world implementation.
Improved Trajectory Tracking for Nonholonomic Mobile Robots Via Dynamic Weight Adjustment in Type-2 Fuzzy Model Predictive Control Hedroug, Mohamed Elamine; Bdirina, El Khansa; Guesmi, Kamel; Nail, Bachir; Tibermacine, Imad Eddine; Ma'arif, Alfian
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.1926

Abstract

This paper presents an advanced methodology for trajectory control of non-holographic mobile robots. It addresses the challenges of dynamic environments and system uncertainty by proposing a fuzzy model predictive control (FMPC) system that combines Type-2 fuzzy logic (F2MPC) with model predictive control (MPC) to enhance tracking accuracy and adaptability.  A Takagi-Sugeno (T-S) fuzzy model changes the MPC weighting matrices in real-time based on speed and distance errors, while the Type-2 fuzzy system handles uncertainties better than Type-1 systems. Tests using circular and wavy trajectories show that the Type-2 Fuzzy MPC (F2MPC) works better than traditional methods, achieving fewer tracking errors (Integral Squared Error of 0.0011), faster convergence (in 1.2 seconds), and using 65% less energy for movement than conventional MPC. Robustness tests show the controller's stability under disturbances, with minimal deviation and quick recovery. The results highlight the F2MPC's precision, efficiency, and adaptability, making it a promising solution for complex robotic navigation tasks.  The study found that Type-2 fuzzy logic and predictive control improve trajectory tracking, paving the path for real-world applications and computational optimisations.
Indoor Quadcopter Localization Using Fuzzy-Sliding Mode Control for Robust Navigation Darwito, Purwadi Agus; Agustina, Nilla Perdana; Pratama, Detak Yan; Al Farros, Mohammad Naufal; Setiadi, Iwan Cony; Biyanto, Totok Ruki; Imron, Choirul
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.1941

Abstract

Growing demand for warehouse automation requires Unmanned Aerial Vehicles (UAVs), particularly quadcopters, to operate autonomously with a high level of precision and reliability. However, indoor localization poses unique challenges due to the absence of Global Positioning System (GPS) signals, making alternative sensors and robust control strategies essential. This study proposes an indoor UAV navigation system that integrates camera and LiDAR sensors with Fuzzy–Sliding Mode Control (Fuzzy-SMC) to enhance stability and reduce the chattering effects commonly associated with Sliding Mode Control. In the proposed method, the camera provides better accuracy for real-time position tracking compared to LiDAR, while fuzzy logic adaptively adjusts the Sliding Mode Control parameters, which serve as the main controller for stabilizing the quadcopter’s nonlinear dynamics. Research methodology includes mathematical modeling of the UAV quadcopter, the design of the Fuzzy-SMC controller, and simulation-based testing for trajectory tracking in indoor environments. Results show that the developed system achieves high accuracy, with error values ranging from 0 to 4.044%, remaining below the acceptable threshold of 5%. These findings demonstrate that integration of a camera with Fuzzy-SMC provides an effective and reliable solution for indoor quadcopter UAV navigation, while future research will focus on optimizing the fuzzy rule base and conducting hardware validation in real warehouse scenarios.
Chess Optimizer for Load Frequency Control of Three-Area Multi-Source Renewable Energy Based on PID Plus Second Order Derivative Controller Areeyat, Chatmongkol; Audomsi, Sitthisak; Obma, Jagraphon; Yang, Xiaoqing; Sa-Ngiamvibool, Worawat
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.2052

Abstract

Renewable energy sources such as solar and wind are increasingly integrated into multi-area power systems. However, their fluctuating and unpredictable characteristics pose challenges for sustaining system stability. Therefore, automatic generation control (AGC) is essential for the continual regulation of power and frequency in the system. This article presents the use of a Proportional–Integral–Derivative plus second-order derivative (PID+DD) controller for load frequency control in a three-area multi-source power system, which includes a thermal reheat power plant with a generation rate constraint (GRC) representing the maximum permissible change rate of generation output of 5% per min , a hydroelectric power plant with a  GRC of 370% per min, and a wind power plant where wind speeds vary across areas. The power generation ratio of the three areas is 1:2:4. The controller parameters were tuned using a Chess Optimizer (CO), a metaheuristic inspired by chess move complexity and planning, with specific weights assigned to each type of chess piece. Two load change scenarios were studied: a 10% step load perturbation (10% SLP) and a random load pattern (RLP).  Furthermore, experimental results based on the Integral of Time-weighted Absolute Error (ITAE) indicate that the PID+DD controller tuned by the Chess Optimizer achieved the lowest steady-state error in both scenarios (10% SLP and RLP). In Case 1 (SLP), it achieved an ITAE of 25.5072, representing a 9.70% reduction compared to the PID controller and a 1.96% reduction compared to the PI controller. In Case 2 (RLP), it achieved an ITAE of 88.0654, representing a 1.14% reduction compared to the PID controller and a 2.03% reduction compared to the PI controller. These improvements contribute to enhanced oscillation damping, reduced overshoot and undershoot, and improved frequency stability, demonstrating the practical applicability of the proposed approach in future smart grids with high renewable energy penetration.
A Combination of HHO and BEI Techniques for Frequency Control in Renewable-Dominated Microgrids: Towards Advancing Sustainable Development Elnaggar, Mohamed F.
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.1953

Abstract

To address the rising need for resilient and eco-friendly power systems, this research presents an intelligent load frequency control (LFC) framework specifically designed for hybrid microgrids with significant renewable energy integration and variable operational dynamics. The proposed control scheme leverages the Harris Hawks Optimization (HHO) algorithm in conjunction with a Balloon Effect (BE) adaptation mechanism, enabling real-time tuning of controller parameters in response to system fluctuations and disturbances. The simulation model encompasses a diverse hybrid microgrid configuration, comprising PV arrays, a diesel generator, and time-varying load profiles. Performance assessments were conducted across three operating modes: diesel-alone supply, coordinated diesel-PV operation, and a high-renewable scenario incorporating uncertainties in system inertia, damping, and droop. In all tested cases, the HHO + BE controller demonstrated superior behavior compared to standard optimization techniques like GTO, SCA, and WOA, exhibiting quicker stabilization, smaller frequency deviations (down to ±0.18 Hz), and minimized control actions. Overall, this study underscores the adaptability and reliability of the HHO + BE control approach for maintaining frequency stability in modern, low-inertia microgrids. The results offer compelling evidence of its effectiveness in real-time applications, particularly in environments increasingly dominated by fluctuating renewable energy sources and uncertain operating conditions.

Page 3 of 3 | Total Record : 25

 1   2   3 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 5, No 3 (2025) Vol 5, No 2 (2025) Vol 5, No 1 (2025) Vol 4, No 4 (2024) Vol 4, No 3 (2024) Vol 4, No 2 (2024) Vol 4, No 1 (2024) Vol 3, No 4 (2023) Vol 3, No 3 (2023) Vol 3, No 2 (2023) Vol 3, No 1 (2023) Vol 2, No 4 (2022) Vol 2, No 3 (2022) Vol 2, No 2 (2022) Vol 2, No 1 (2022) Vol 1, No 4 (2021) Vol 1, No 3 (2021) Vol 1, No 2 (2021) Vol 1, No 1 (2021) More Issue